AIMC Topic: Tomography, X-Ray Computed
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Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries.
Computer methods and programs in biomedicine
Jan 31, 2025
PURPOSE: In medical deep learning, models not trained from scratch are typically fine-tuned based on ImageNet-pretrained models. We posit that pretraining on data from the domain of the downstream task should almost always be preferable.
Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Jan 30, 2025
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Humans
Machine Learning
Prognosis
Hepatectomy
Bile Duct Neoplasms
Nutritional Status
Preoperative Period
Intra-Abdominal Fat
Serum Albumin
Cholangiocarcinoma
Subcutaneous Fat
Nutrition Assessment
Postoperative Complications
Neoplasm Recurrence, Local
Bilirubin
Survival Rate
Male
Middle Aged
Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis.
International journal of medical informatics
Jan 30, 2025
BACKGROUND: With advancements in medical technology and science, chronic obstructive pulmonary disease (COPD), one of the world's three major chronic diseases, has seen numerous remarkable outcomes when combined with artificial intelligence, particul...
Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.
Medical physics
Jan 30, 2025
BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.
Predictive estimation of ovine hip joint centers: Neural networks vs. linear regression.
Journal of biomechanics
Jan 30, 2025
The purpose of this study was to investigate the utility of neural networks to estimate the hip joint center location in sheep and compare the accuracy of neural networks to previously developed linear regression models. CT scans from 16 sheep of var...
Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.
European journal of radiology
Jan 30, 2025
OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.
The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.
Radiography (London, England : 1995)
Jan 30, 2025
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.
European journal of radiology
Jan 29, 2025
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC cancer
Jan 29, 2025
BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.
Investigative radiology
Jan 28, 2025
OBJECTIVE: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based ite...
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